
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
		import torch


lowercase    :   Optional[Any]						=			logging.get_logger(__name__)




@dataclass
class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Optional[int]	,  a						:Tuple=False	,  a						:Optional[Any]=False	,  a						:Union[str, Any]=6.0	,  a						:Optional[Any]=None	,  a						:int=False	,  a						:str=False	,  a						:Tuple=None	,  a						:List[str]="fp4"	,  a						:Tuple=False	,  **a						:int	,  )						->					List[str]:
								__UpperCamelCase      :  Optional[Any]       =   load_in_abit
								__UpperCamelCase      :  Optional[int]       =   load_in_abit
								__UpperCamelCase      :  List[str]       =   llm_inta_threshold
								__UpperCamelCase      :  List[str]       =   llm_inta_skip_modules
								__UpperCamelCase      :  Dict       =   llm_inta_enable_fpaa_cpu_offload
								__UpperCamelCase      :  Union[str, Any]       =   llm_inta_has_fpaa_weight
								__UpperCamelCase      :  Any       =   bnb_abit_quant_type
								__UpperCamelCase      :  Tuple       =   bnb_abit_use_double_quant

								if bnb_abit_compute_dtype is None:
															__UpperCamelCase      :  int       =   torch.floataa
								elif isinstance(a	,  a		):
															__UpperCamelCase      :  Tuple       =   getattr(a	,  a		)
								elif isinstance(a	,  torch.dtype		):
															__UpperCamelCase      :  List[str]       =   bnb_abit_compute_dtype
								else:
															raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype"		)

								self.post_init()







	def   _lowerCamelCase							(   self						:int		)						->					List[str]:
								if not isinstance(self.llm_inta_threshold	,  a		):
															raise ValueError("llm_int8_threshold must be a float"		)

								if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules	,  a		):
															raise ValueError("llm_int8_skip_modules must be a list of strings"		)
								if not isinstance(self.llm_inta_enable_fpaa_cpu_offload	,  a		):
															raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean"		)

								if not isinstance(self.llm_inta_has_fpaa_weight	,  a		):
															raise ValueError("llm_int8_has_fp16_weight must be a boolean"		)

								if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype	,  torch.dtype		):
															raise ValueError("bnb_4bit_compute_dtype must be torch.dtype"		)

								if not isinstance(self.bnb_abit_quant_type	,  a		):
															raise ValueError("bnb_4bit_quant_type must be a string"		)

								if not isinstance(self.bnb_abit_use_double_quant	,  a		):
															raise ValueError("bnb_4bit_use_double_quant must be a boolean"		)

								if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes"		)		) >= version.parse(
								    "0.39.0"		):
															raise ValueError(
															    "4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version"		)







	def   _lowerCamelCase							(   self						:Any		)						->					Any:
								return self.load_in_abit or self.load_in_abit







	def   _lowerCamelCase							(   self						:Optional[int]		)						->					Optional[int]:
								if self.load_in_abit:
															return "llm_int8"
								elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
															return "fp4"
								elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
															return "nf4"
								else:
															return None







	@classmethod
	def   _lowerCamelCase							(   cls						:Dict	,  a						:str	,  a						:int	,  **a						:List[Any]		)						->					str:
								__UpperCamelCase      :  List[str]       =   cls(**a		)

								__UpperCamelCase      :  List[str]       =   []
								for key, value in kwargs.items():
															if hasattr(a	,  a		):
																						setattr(a	,  a	,  a		)
																						to_remove.append(a		)
								for key in to_remove:
															kwargs.pop(a	,  a		)

								if return_unused_kwargs:
															return config, kwargs
								else:
															return config







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:Union[str, os.PathLike]		)						->					Union[str, Any]:
								with open(a	,  "w"	,  encoding="utf-8"		) as writer:
															__UpperCamelCase      :  Dict       =   self.to_dict()
															__UpperCamelCase      :  Any       =   json.dumps(a	,  indent=2	,  sort_keys=a		) + "\n"

															writer.write(a		)







	def   _lowerCamelCase							(   self						:List[Any]		)						->					Dict[str, Any]:
								__UpperCamelCase      :  Any       =   copy.deepcopy(self.__dict__		)
								__UpperCamelCase      :  str       =   str(output["bnb_4bit_compute_dtype"]		).split("."		)[1]

								return output







	def __repr__(   self						:int		)						->					Dict:
								return f'{self.__class__.__name__} {self.to_json_string()}'







	def   _lowerCamelCase							(   self						:Optional[Any]	,  a						:bool = True		)						->					str:
								if use_diff is True:
															__UpperCamelCase      :  Tuple       =   self.to_diff_dict()
								else:
															__UpperCamelCase      :  List[str]       =   self.to_dict()
								return json.dumps(a	,  indent=2	,  sort_keys=a		) + "\n"







	def   _lowerCamelCase							(   self						:Dict		)						->					Dict[str, Any]:
								__UpperCamelCase      :  Any       =   self.to_dict()

								# get the default config dict
								__UpperCamelCase      :  Any       =   BitsAndBytesConfig().to_dict()

								__UpperCamelCase      :  Tuple       =   {}

								# only serialize values that differ from the default config
								for key, value in config_dict.items():
															if value != default_config_dict[key]:
																						__UpperCamelCase      :  int       =   value

								return serializable_config_dict

from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip